Hypothesis 1 calls for the analysis of the
systematic risk (beta) of the firms involved in the ranking. Beta
is a measurement of the sensitivity of a companys stock
price to the overall fluctuations in the stock market, proxied
here by the Standard & Poors 500 Index Price for
Industrial Companies. For example, a beta of 1.5 indicates that a
companys stock price tends to rise (fall) 1.5% with a 1%
rise (fall) in the index price. Beta is calculated here for a
5-year (60-month) time period, ending in the ranking month. If
less price history is available, beta is calculated for as few as
24 months. Month-end closing prices, including dividends
received, are used in the calculation. The resulting average
betas for each event month around the ranking date are presented
in exhibit 3. Beta is shown to hover in the 1.25 to 1.43 range,
significantly greater than the market average of 1.00. These
figures support hypothesis 1 and, by direct implication, the use
of risk-adjusted returns in complement to the simpler market
adjustment, which would be upward-biased in this case.

Monthly Abnormal Returns

The next step in the analysis consists in
analyzing the monthly abnormal returns around the respective
ranking dates, using the various adjustments mentioned in the
methodology section. An examination of the data indicates that a
large proportion of the monthly returns appear to be on the
positive side following the rankings, but inferences are not
particularly easy from this type of statistic. Instead,
cumulative returns should be used over the post-ranking period.
These results are presented in exhibit 4.

Individual stocks are tracked for 36 months
post-ranking. Two features are particularly striking in the
long-term performance of the shares. First of all, the Inc.100
rankings are followed by a significant "dip", or a
period of abnormally negative returns. This temporary
"underperformance" is particularly evident when using
the risk-adjusted returns and appears to last for about 10 to 12
months following the rankings. This, in and of itself, would tend
to support the notion that a share appearing in the Inc.100
rankings may be "overbought" by investors chasing the
next "hot" company, resulting in prices that are not
sustainable over time. The market correction results in the
negative abnormal returns observed through month 10.

Second, following these negative returns, firms
tend to experience statistically significant positive abnormal
returns. These returns would tend to support an alternative
interpretation: that the market is actually underestimating the
future growth potential of the firms listed in the rankings or
overestimating their risk and, accordingly, systematically
underpricing them. Strategies consisting of purchasing stocks
listed in the Inc.100 rankings in the month of the ranking and
holding them over 36 months thus generate raw returns of
approximately 68% over the period, or some 40% in excess of what
would have been expected given the level of risk assumed in the
strategy. The t-test results for the most conservative of the
adjustments, the market-based correction, indicate that the
returns for the first 36 post-ranking months are significant at
the 1% level of confidence.

These cumulative abnormal returns indicate the
apparent inability of the market to properly price supergrowth
stocks, leaving ample opportunities for arbitrage profits, either
short-selling the list over a short horizon (about 6 months) or
buying the stocks and holding them over the long term (up to 36
months).

A critical factor to consider in implementing
such simple arbitrage strategies is whether or not the pattern of
long-term underpricing is robust with respect to the year of the
ranking. To formally test for such factor, long-term cumulative
abnormal returns were first analyzed on a year-by-year basis for
the same sample of Inc.100-ranked firms. The results of the
analyses using unadjusted raw returns indicate that even though
the pattern seems very much present in almost all ranking years,
the 1985, 1986 and 1987 cohorts would not have been such great
investments over the 36-month horizon considered here. These
three cohort years actually include the effect of the broader
1987 stock market crash in their long-term performance.

As mentioned earlier, raw returns are not a
proper measure to account for the systematic risk of these
supergrowth firms. Exhibit 5
presents the same information but on
a market-adjusted basis. Once again, the cohort years 1984, 1985,
1986 and 1987 seem to have been affected by the 1987 stock market
crash.

Regression Analyses

The empirical analyses performed above
pertained to determining to what extent simple strategies based
on public information, in this case the Inc.100 ranking of the
fastest growing public companies in America, could be used to
generate returns in excess of what a normal risk/return
relationship would require. Such deviations are interpreted as
supporting the inefficiency of the market, i.e. its inability to
properly price stocks characterized by high systematic risks
(beta) and extremely large historical growth rates.

A final step involves the investigation of the
factors that may explain the abnormal returns observed in a
classic regression methodology. Possible explanatory factors for
the cumulative abnormal returns over the 20 months post-ranking
include the growth in sales over the previous 5 years (GSALES5),
the growth in net income (GNI5), the growth in the number of
employees (GEMPLOY5), the market-to-book ratio at the time of the
ranking (MKBKR0), the price-earning ratio at the time of the
ranking (PEM0), the owners salary (SALARY) or equity
ownership (EQUITY) in the firm, and whether the current CEO is
also the company founder (CEOFD).

Before running these regressions, it is
important to determine to what extent these possible explanatory
or control factors are actually correlated in order to detect the
existence of a multicolinearity problem. The Pearson correlation
coefficients between the factors outlined above are detailed in
exhibit 6
with the degrees of confidence for rejection of H0
: the coefficient of correlation is equal to zero. As
anticipated, a number of the variables appear to be correlated
significantly, such as GSALES5, GNI5, and GEMPLOY5. On the other
hand, SALARY and EQUITY appear to be less correlated. Both
market-to-book and price-to-earning ratios have little
correlation with the other factors and have not been included in
the table.

With this multicolinearity problem, it will be
difficult to evaluate the real explanatory power of each of the
correlated variables on the long-term performance of the
supergrowth firms in the sample. None of the regressions
performed, with either single explanatory factors or combinations
thereof, indicate significant relationships. In other words, none
of the growth-related historical factors, such as past growth in
earnings, sales, net income, employment, or time of ranking
appear to significantly determine the stock price performance
following the Inc.100 rankings. The only two forward-looking
variables (market-to-book and price-to-earning ratios at the time
of ranking) usually appear in the regressions with negative
coefficients, indicating that the firms with the largest ratios
actually did worse on the long-term performance variable, but
none of these coefficients are statistically different from zero.
Hypothesis 3 does not seem to be supported.

The initial objective of the study was to
determine the extent to which the market is able to properly
price stocks characterized by very high historical growth rates,
what is referred to here as "supergrowth" stocks in
deference to the more commonly known "growth" stocks. A
number of arguments can be made to support possible market
inefficiencies under these limit conditions. The finance
literature focuses on the importance of growth rates and
systematic risk in pricing shares, both factors which are likely
to be difficult to evaluate for firms having experienced
explosive growth in the last five years. The psychology
literature, and in particular, its subset studying human
inferences and its biases, highlights the tendency for
individuals to diverge from pure rationality, for example by
attributing larger probabilities than deserved to events
relatively close in time to the present. In other words, humans
may not be perfect Bayesian updaters, letting a number of biases
taint their inferential processes.

In order to test market efficiency in these
conditions, the returns to simple investment portfolio strategies
based on public information are investigated. The portfolios
consist of shares in the firms listed in the Inc.100 Ranking of
the Fastest Growing Public Companies in America. These portfolios
are assembled when the rankings are published and held for
various periods of time. The analyses conducted here indicate
that significant abnormal returns are generated for these
strategies in excess of what would normally be required to
compensate for the level of risk incurred. Although the tests
could potentially be affected by a form of survivorship bias,
supplementary cohort analyses indicate that this is unlikely to
be the case here. Cross-sectional regressions were not able to
single out significant explanatory factors for the long-term
performance of these investment strategies.

The implications of this initial study are
enormous, for both investors and issuers. If indeed the market is
not able to properly price high-growth entities, a fact long
theorized by growth and new venture specialists, then significant
abnormal returns could be earned by simple trading rules. From a
company standpoint, such inefficiencies essentially indicate that
"windows of opportunity" indeed exist in the market for
issuing new shares, a view again long-supported by investment
bankers and issuers alike. In other words, periods of overpricing
and underpricing of shares exist, justifying the recourse to,
respectively, new issuances or stock repurchases.

The existence of "pockets" of
inefficiency in the market in its high-growth segments puts a
serious cap on the generally accepted concept of efficiency as a
whole. If indeed the market is efficient under
"reasonable" conditions, deviations from that norm
(such as those resulting from explosive growth, bankruptcies,
liquidations, major catastrophes, etc.) seem to quickly stretch
the ability of the market to analyze and incorporate the new
information into the prices. These delayed responses or
mispricings open up the door to strategic behavior by issuers and
investors alike, something most financial actors have long
supported but could not be accommodated by the classical market
efficiency paradigm.